Artificial intelligence intra-operative surgical guidance system

ABSTRACT

The inventive subject matter is directed to an artificial intelligence intra-operative surgical guidance system and method of use. The artificial intelligence intra-operative surgical guidance system is made of a computer executing one or more automated artificial intelligence models trained on data layer datasets collections to calculate surgical decision risks, and provide intra-operative surgical guidance; and a display configured to provide visual guidance to a user.

FIELD OF THE INVENTION

The subject of this invention is an artificial intelligenceintraoperative surgical guidance in joint replacements, spine, traumafracture reductions and deformity correction and implantplacement/alignment. A method is provided for analyzing subject imagedata, calculating surgical decision risks and autonomously providingrecommended pathways or actions that support the decision-making processof a surgeon to predict optimized implant and subject outcomes (ex.implant guidance, fracture reduction, anatomical alignment) by agraphical user interface.

BACKGROUND OF THE INVENTION

Many of the radiographic parameters essential to total hip arthroplasty(THA) model performance, such as wear and stability, can be assessedintraoperatively with fluoroscopy. However even with intraoperativefluoroscopic guidance, the placement of an implant or the reduction of abone fragment can still not be as close as desired by the surgeon. Forexample, mal-positioning of the acetabular model during hip arthroplastycan lead to problems. For the acetabular implant to be inserted in theproper position relative to the pelvis during hip arthroplasty requiresthat the surgeon know the position of the patient's pelvis duringsurgery. Unfortunately, the position of the patient's pelvis varieswidely during surgery and from patient to patient. During traumasurgery, proper fracture management, especially in the case of anintra-articular fracture, requires a surgeon to reduce the bone fragmentoptimally with respect to the original anatomy in order to: provide theanatomical with joint the best chance to rehabilitate properly; minimizefurther long-term damage; and, if possible, to regain its normalfunction. Unfortunately, in a fracture scenario, the original anatomicalposition of these bone fragments has been compromised and their naturalrelationship with the correct anatomy is uncertain and requires thesurgeon to use his/her best judgment in order to promote a successfulrepair and subsequent positive outcome. During a surgery, a surgeon isrequired to make real-time decisions that can be further complicated bythe fact that there are multiple decisions needing to be made at thesame time. At any given time, there can be a need for a decision made ona fracture reduction guidance for example and simultaneously a decisionrequired on implant placement and an error at any stage will likelyincrease the potential for a sub-optimal outcome and potential surgicalfailure. Unfortunately, most of these problems are only diagnosed anddetected postoperatively and oftentimes lead to revision surgery. Theserisks and patterns need to be identified in real-time during thesurgical or medical event. As surgeons and medical professionals mustoften rely solely on themselves to identify hazards and risks or makedecisions on critical factors in, and surrounding, a surgical event, aneed exists for a system and method that can provide intraoperativeautomated intelligence guided surgical and medical situational awarenesssupport and guidance.

SUMMARY OF THE INVENTION

This summary describes several embodiments of the presently disclosedsubject matter and, in many cases, lists variations and permutations ofthese embodiments. This summary is merely exemplary of the numerous andvaried embodiments. Mention of one or more representative features of agiven embodiment is likewise exemplary. Such an embodiment can typicallyexist with or without the feature(s) mentioned; likewise, those featurescan be applied to other embodiments of the presently disclosed subjectmatter, whether listed in this summary or not. To avoid excessiverepetition, this summary does not list or suggest all possiblecombinations of such features.

The novel subject matter includes an artificial intelligenceintra-operative surgical guidance system made of: a computing platformconfigured to execute one or more automated artificial intelligencemodels, wherein the one or more automated artificial intelligence modelsare trained on data from a data layer, wherein the data layer includesat least surgical images, to calculate intra-operative surgical decisionrisks, and to provide an intra-operative surgical guidance to a user.More specifically, the computing platform is configured to provide anintra-operative visual display to the user showing surgical guidance.The computing platform is trained to show intra-operative surgicaldecision risks by applying an at least one classification algorithm.

The novel subject matter further includes method for generating adynamic guidance indicator for use in a surgical procedure. The methodincludes the steps of: receiving an intra-operative image of a subject;generating a grid data predictive map; wherein the grid data predictivemap is generated by an artificial intelligence engine; and aligning theintra-operative image with the grid data predictive map to generate adynamic guidance indicator. In one exemplary embodiment, the dynamicguidance indicator is made of a first color for sub-optimal positioningand second color for optimal positioning of an implant or bonealignment.

The novel subject matter further includes a computer-implemented methodfor providing surgical guidance. The method steps include obtainingsubject image data made of: a preoperative image of a nonoperative sideof a subject's anatomy and an intraoperative image of an operative sideof the subject's anatomy; dynamically displaying the subject image dataon a graphical user interface; selecting an anatomical structure withinthe subject image data and mapping a grid template to the anatomicalstructure to register an image for the nonoperative side of thesubject's anatomy with an image of the intraoperative image of theoperative side of the subject's anatomy to provide a registeredcomposite image; providing a computing platform is made of: anartificial intelligence engine and at least one dataset configured togenerate a surgical guidance; providing as a data output, the registeredcomposite image to the artificial intelligence engine to generate an atleast one surgical guidance; and dynamically updating, by the computingplatform, the registered composite image with the at least one surgicalguidance. The surgical guidance can include robotic synchronization, acutting block, an Internet of Things (IoT) device, and a trackableguide.

The novel subject matter includes: a computer-implemented method forartificial intelligence based surgical guidance. The novel methodincludes the steps of: providing a computing platform made of anon-transitory computer-readable storage medium coupled to amicroprocessor, wherein the non-transitory computer-readable storagemedium is encoded with computer-readable instructions that implementfunctionalities of a plurality of modules, wherein the computer-readableinstructions are executed by a microprocessor. The novel method includesthe steps of: receiving an at least one preoperative image of a subject;computing an image quality score using an Image Quality Scoring Module;accepting or rejecting the preoperative image based on quality scoregenerated by a Pose Guide Module, if the at least one preoperative imageis accepted; correcting for distortion in the at least one preoperativeimage; annotating an at least one anatomical landmark in thepreoperative image using an Image Annotation Module to provide an atleast one annotated preoperative image; storing the at least oneannotated preoperative image in a preoperative image database; receivingan at least one intraoperative image; computing image quality scoreusing an Image Quality Scoring Module; accepting or rejecting the atleast one intraoperative image based on quality score generated by aPose Guide Module, if the at least one intraoperative image is accepted;correcting for distortion in the at least one intraoperative image;annotating an at least one anatomical landmark using an Image AnnotationModule to provide an at least one annotated intraoperative image;registering the at least one annotated intraoperative image to a bestmatching image in the preoperative image database; computing a matchingscore using an image registration; if accepted; estimating athree-dimensional shape of an implant or anatomy using a 3D ShapeModeling Module; mapping an alignment grid to the annotated imagefeatures using an Image Registration Module to form a composite imageand displaying the composite image on a graphical user interface; anddynamically updating, by the computing platform, the composite image toprovide an at least one surgical guidance.

More specifically, the method further includes the steps of: receivingan at least one postoperative image of the subject by the computingplatform; computing an image quality score of the at least onepostoperative image using an Image Quality Scoring Module; accepting orrejecting the at least one postoperative image based on quality scoregenerated by a Pose Guide Module, if image is accepted; correcting fordistortion in the at least one post-operative image; annotating an atleast one image anatomical landmark using an image annotation module toprovide an at least one postoperative annotated image; registering theat least one postoperative annotated image to a prior image in apostoperative image database and computing matching scores; computing amatching score using an image registration metric; if accepted;estimating a three-dimensional shape of an implant or an anatomy using a3D Shape Modeling Module; mapping an alignment grid to an annotatedimage features using the Image Registration Module; displaying acomposite image on the graphical user interface; computing outcomeprobability score using the Postoperative Outcomes Prediction Model; anddisplaying the composite image on the graphical user interface; anddynamically updating, by the computing platform, the composite imagewith an outcome prediction guidance.

The inventive subject matter further includes a method to provide anorthopedic surgeon conducting an alignment or fixation procedure with avisual display configured to provide intra-operative surgical guidanceto the orthopedic surgeon. This method includes the steps of providing acomputing platform, wherein the computing platform is further comprisedof: a plurality of datasets and at least one outcomes prediction modulecomprised of multiple trained classifiers each with a weightedcontribution to a surgical outcome prediction for an alignment orfixation procedure and providing a visual display configured to providethe intra-operative surgical guidance to a the orthopedic surgeonconducting an alignment or fixation procedure.

BRIEF DESCRIPTION OF THE SEVERAL IMAGES OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The drawings show schematically a fluoroscopicalignment plate apparatus and method of use according to an example formof the present invention. The invention description refers to theaccompanying drawings:

FIG. 1A is a diagram of the system for automated intraoperative surgicalguidance.

FIG. 1B shows an exemplary view of a head-up display image of thesystem.

FIG. 2A is a diagram of the computing platform.

FIG. 2B is a diagram of an artificial intelligence computing system.

FIG. 3A. is a schematic illustration of Deep Learning as applied toautomated intraoperative surgical guidance.

FIG. 3B is a schematic illustration of reinforcement learning automatedintraoperative surgical guidance.

FIG. 4A is a block diagram of the software modules.

FIG. 4B is a data-set flow-chart.

FIG. 5A is an overview of preoperative workflow of the presentinvention.

FIG. 5B is an overview of intraoperative workflow of the presentinvention.

FIG. 5C is an image of a postoperative workflow of the presentinvention.

FIG. 6 is a preoperative image.

FIG. 7A is a preoperative image shown with procedure specificapplication relevant information as the input with the resultant tasksand actions.

FIG. 7B is the output of the anatomical position model showing a guidetemplate of good pose estimation.

FIG. 8A is a graphical user interface showing image with grid templateshowing the anatomical outlines of what is a good pose.

FIG. 8B shows the best image pose guidance process.

FIG. 8C shows the output of the use of a reference image.

FIG. 9A is a graphical user interface showing an image with anatomicalfeatures defined.

FIG. 9B shows user inputs, task and actions.

FIG. 10 is a graphical user interface showing anatomical measurementgrid positioned on an image.

FIG. 11A is a graphical user interface showing an image of the affectedside.

FIG. 11B shows user inputs, task and actions.

FIG. 12A is a graphical user interface showing a display of acceptedimage classification.

FIG. 12B shows user inputs, task and actions relating to the formulationof a treatment plan.

FIG. 13A is a graphical user interface showing ghosting.

FIG. 13B shows the data flow in ghosting.

FIG. 14A is a graphical user interface showing grid similarity withmatch confidence display.

FIG. 14B shows the output of a graphical user interface showingconfirmation of good-side for further use.

FIG. 14C shows the good side acceptance.

FIG. 15A is a graphical user interface showing an image of the good-sideoverlay with grid alignment and measurements.

FIG. 15B is a graphical user interface showing an image of the good-sideoverlay with grid alignment and measurements for an ankle.

FIG. 15C is a graphical user interface showing an image of the good-sideoverlay with grid alignment and measurements for a nail.

FIG. 16 is a representation of statistical inference of 3D models and anexample of the use of representation of statistical inference of 3Dmodels.

FIG. 17A shows an image of anatomy alignment and fracture reductionguidance.

FIG. 17B shows user inputs, task and actions related to the datasets.

FIG. 18A is a graphical user interface instrument guidance.

FIG. 18B shows user inputs, task and actions related to the datasets.

FIG. 18C shows various outputs

FIG. 18D shows the predictive and contra-side matched ideal entry point.

FIG. 19A is a graphical user interface showing instrument guidance andor virtual implant placement.

FIG. 19B is a graphical user interface showing instrument guidance andor virtual implant placement of lag screw placement.

FIG. 20A shows user output related to lag screw placement.

FIG. 20B shows predictor variable using a domain knowledge related tolag screw placement.

FIG. 21 shows user output related to lag screw placement usingIntraoperative, Real-time Situational Guidance.

FIG. 22 shows a graphical user interface, user inputs, task and actionsrelating to problem prediction related to an ankle problem.

FIG. 23 shows a graphical user interface, user inputs, task and actionsrelating to problem prediction related to a hip arthroplasty.

FIG. 24 shows a graphical user interface, user inputs, task and actionsrelating to problem prediction related to a knee arthroplasty.

FIG. 25 shows a graphical user interface, user inputs, task and actionsrelating to problem prediction related to a spine.

FIG. 26A shows a graphical user interface, user inputs, task and actionsrelating to problem prediction related to a sports medicine example.

FIG. 26B shows a graphical user interface, user inputs, task and actionsrelating to problem prediction related to a hip preservation (PAO orFAI) example.

FIG. 27 shows input and output relating to problem prediction.

FIG. 28A shows a graphical user interface showing implant and percentperformance.

FIG. 28B Graphical interface demonstrating the prediction of an optimalversus sub-optimal outcome.

FIG. 28C illustrates a representative example for task workflowdemonstrating decision support process with the AI model.

FIG. 29 shows an output relating to image interpretation performed oninput images.

FIG. 30 shows the optimal alignment and Implant position achieved basedon image analysis and interpretation

FIG. 31 shows a “heat map” where sub-optimal positioning regions on thegrid map are indicated in red and optimal positioning regions indicatedwith green to guide the surgical process.

DESCRIPTION OF EXEMPLARY EMBODIMENTS

The present invention can be understood more readily by reference to thefollowing detailed description of the invention. It is to be understoodthat this invention is not limited to the specific devices, methods,conditions or parameters described herein, and that the terminology usedherein is for describing embodiments by way of example only and is notintended to be limiting of the claimed invention. Also, as used in thespecification including the appended claims, the singular forms “a,”“an,” and “the” include the plural, and reference to a numerical valueincludes at least that value, unless the context clearly dictatesotherwise. Ranges can be expressed herein as from “about” or“approximately” one value and/or to “about” or “approximately” anothervalue. When such a range is expressed, another embodiment includes fromthe one value and/or to the other value. Similarly, when values areexpressed as approximations, by use of the antecedent “about,” it willbe understood that the value forms another embodiment. All combinationsof method or process steps as used herein can be performed in any order,unless otherwise specified or clearly implied to the contrary by thecontext in which the referenced combination is made. These and otheraspects, features and advantages of the invention will be understoodwith reference to the detailed description herein and will be realizedby means of the various elements and combinations particularly pointedout in the appended claims. It is to be understood that both theforegoing general description and the following detailed description ofthe invention are exemplary and explanatory of preferred embodiments ofthe inventions and are not restrictive of the invention as claimed.Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention belongs.

The following system and method generally relate to a computing platformhaving a graphical user interface for displaying subject image data andapply data science techniques such as machine and deep learning to:calculate surgical decision risks, to predict a problem and provideguidance in real-time situations. The system autonomously displaysrecommended actions through a display such as graphical user interfaceto provide an optimized implant and subject outcome by calculating theprobability of a successful procedural outcome (ex. Implant guidance,fracture reduction, anatomical alignment). The inventive subject matteris directed to an artificial intelligence intra-operative surgicalguidance system and method of use. The system in its most basic formincluded: a computer executing one or more automated artificialintelligence models trained on at least intra-operative surgical images,to calculate surgical decision risks, and to provide an intra-operativesurgical guidance, and a visual display configured to provide theintra-operative surgical guidance to a user.

Artificial Intelligence is the ability of machines to perform tasks thatare characteristics of human intelligence. Machine learning is a way ofachieving Artificial Intelligence. AI is the ability of machines tocarry out tasks in an intelligent way. Machine learning is anapplication of Artificial Intelligence that involves a data analysis toautomatically build analytical models. Machine learning operates on thepremise that computers learn statistical and deterministicclassification or prediction models from data; the computers and theirmodels adapt independently as more data is inputted to the computingsystem. Misinterpretation of data can lead to mistakes and ultimately afailed outcome. Artificial Intelligence can integrate and infer from amuch larger and smarter dataset than any human can discerning patternsand features that are difficult to appreciate from a human perspective.This becomes particularly relevant in the alignment of anatomy andcorrect placement of implants. The system analyzes and interprets theinformation and provides guidance based upon a correlation to a knownset of patterns and inference from novel sets of data. The artificialintelligence intra-operative surgical guidance system is made of acomputer executing one or more automated artificial intelligence modelstrained on data layer datasets collections to calculate surgicaldecision risks, and provide intra-operative surgical guidance; and adisplay configured to provide visual guidance to a user.

Now referring to FIGS. 1A and 1B, the artificial intelligenceintra-operative surgical guidance system 1 includes an imaging system110. The imaging system 110 receives subject image data such as images120 (radiographic, ultrasound, CT, MRI, 3D, terahertz) of a subject'sanatomy. Exemplary medical images that can be analyzed forintraoperative surgical guidance can include a radiographic image suchas an image generated by portable fluoroscopy machine called a C-arm. Insome embodiments, the computing platform 100 can be configured toperform one or more aspects associated with automated intraoperativesurgical guidance in medical images. For example, computing platform 100and/or a related module can receive an inter-operative surgical image,e.g., a fluoroscopic image of the knee.

The artificial intelligence intra-operative surgical guidance system 1includes an input of a series of x-ray or fluoroscopic images of aselected surgical site, a computing platform 100 to process the surgicalimages and an overlay of a virtual, augmented, or holographicdimensioned grid, with an output to an electronic display device 150.The electronic display device 150 provides a displayed composite imageand graphical user interface 151. The graphical user interface 151 isconfigured to: allow manipulation of a dimensioned grid 200 by a user155, such as a surgeon, physician assistant, surgical scrub nurse,imaging assistant and support personnel. The computing platform 100 isconfigured to synchronize with a sensor 130 to (a) provideintraoperative anatomical (for example, bone) or implant positionalinformation; and (b) provide postoperative anatomical or implant orexternal alignment and correction device information to an ArtificialIntelligence Engine for guidance.

The computing platform 100 is configured to synchronize with a surgicalfacilitator 160 such as a robot or a haptic feedback device 162 toprovide the same predictive guidance as described throughout as anenabler for robotic surgery. The computing platform 100 is configured tosynchronize with an intelligence guided trackable capable of creatingaugmented grids or avatars of implants, instruments or anatomy toprovide the same predictive guidance as described throughout as anenabler for intelligence guided artificial reality trackable navigation.

The system components include an input of a series of x-ray orfluoroscopic images of a selected surgical site, a dynamic surgicalguidance system 1 to process the surgical images and an overlay of avirtual, augmented, or holographic dimensioned grid 200 with an image120, with an input device to provide manipulation of the dimensionedgrid 200 by a user 155, such as a surgeon. In one embodiment, theelectronic display device 150 is an electronic display device, such as acomputer monitor, or a heads-up display, such as GLASS (Google). Inanother embodiment, the electronic display screen 150 is a video fpvgoggle. An out-put to an electronic display device 150 is provided forthe user 155 to image the overlay of the series of images and thedimensioned grid 200.

The augmented reality or holographic dimensioned grid 200 can bemanipulated by the user 155 by looking at anatomic landmarks, the shownon the electronic display device 150 that will facilitate locking on thecorrect alignment/placement of surgical device. The artificialintelligence intra-operative surgical guidance system 1 allows the user155 to see critical work information right in their field-of-image usinga see-through visual display and then interact with it using familiargestures, voice commands, and motion tracking. The data can be stored indata storage. The artificial intelligence intra-operative surgicalguidance system 1 allows the user 155 to see critical work informationin their field-of-image using a see-through visual display device 150and then interact with it using familiar gestures, voice commands, andmotion tracking through a graphical user interface 151 such as by anaugmented reality controller. The graphical user interface 151, such asaugmented reality or holographic dimensioned grid, can be manipulated bythe user 155 by looking at anatomic landmarks, then shown on theelectronic display device 150 that will facilitate locking on thecorrect alignment/placement of surgical device.

FIGS. 2A & 2B are diagrams illustrating an exemplary artificialintelligence surgical guidance system 1 including a computing platform100 for dynamic surgical guidance according to an embodiment of thesubject matter described herein. A computer platform is a system thatincludes a hardware device and an operating system that an application,program or process runs upon,

The subject matter described herein can be implemented in software incombination with hardware and/or firmware. For example, the subjectmatter described herein can be implemented in software executed by atleast one processor 101. In one exemplary implementation, the subjectmatter described herein can be implemented using a computer readablemedium having stored thereon computer executable instructions that whenexecuted by the processor of a computer platform to perform the steps.Exemplary computer readable media suitable for implementing the subjectmatter described herein include non-transitory devices, such as diskmemory devices, chip memory devices, programmable logic devices, andapplication specific integrated circuits. In addition, a computerreadable medium that implements the subject matter described herein canbe located on a single device or computing platform or can bedistributed across multiple devices or computing platforms. As usedherein, the terms “function” or “module” refer to hardware, firmware, orsoftware in combination with hardware and/or firmware for implementingfeatures described herein.

The computing platform 100 includes at least one processor 101 andmemory 102. The computing device can invoke/request one or more serversfrom the Cloud Computing Environment 98 other clinical metadata can beefficiently retrieved from at least one server from the CloudEnvironment if it is sorted in only one server or from separate serversif the dataset was sorted partially in different servers; some outcomesderived from the AI engine can be directly sent and sorted in one ormore servers in the Cloud platform (privacy is preserved).

The computing platform 100 analyzes an image for risk factors that theuser cannot see due to their human inability to interpret anoverwhelming amount of information at any specific moment. If theimplant placement and the alignment does not match this data pattern, itwill create an awareness in this specific situation and provide a hazardalert to the user. Essentially, identifying and predicting problemsahead of the user encountering them. This can lead to avoidance ofcomplications and prevention of errors. The computing platform 100includes a plurality of software modules 103 to receive and processmedical image data, including modules for image distortion correction,image feature detection, image annotation and segmentation, image toimage registration, three-dimensional estimation from two-dimensionalimages, medical image visualization, and one or more surgical guidancemodules that use artificial intelligence models to classify images aspredictive of optimal or suboptimal surgical outcomes. The term dynamicor dynamically means automated artificial intelligence and can includevarious artificial intelligence models such as for example: machinelearning, deep learning, reinforcement learning or any other strategiesto dynamically learn. In a trauma event, such as fracture reduction ordeformity correction, or in an arthroplasty event such as hip or kneeanatomical alignment or bone cut guidance, or in the event of a spineprocedure with implant alignment correction, or in a sports medicineevent with ACL reconstruction alignment, these surgical procedurespecific datasets coupled with domain knowledge that are useful to anevent can be accessed. They will be used to interpret critical failuremode factors of an implant or anatomical alignment and combined willprovide the user with a Failure Risk Score with the output to the useras a confidence percentage recommendation of a suboptimal or optimalperformance metric. This will be presented to the user in the form ofintelligent predictors and scores to support decisions encountered in areal time event.

The software module 103 includes a plurality of layers. The data layer105 is made of a collection of data from various managed distributeddata collection networks. This collection of data represents theknowledge that is necessary to address specific tasks. Data layer(detailed in FIG. 2B) is the collection of data which is the input foralgorithm layers 106, and it contains data sets from different systemswhich makes Data layer 105 a rich source information needed for thedifferent deployment tasks. These data layers 105 include surgical imagedata and related outcomes, medical advisor knowledge base and sensordata.

The algorithm layer 106 includes computer-implemented methods speciallydesigned to target application layer using inputs provided in Data Layer105. The algorithm layer 106 can also be referred to as an AI engine.The algorithm layer 106 includes various image processing algorithmswhich use different machine learning techniques (engineering features)and artificial intelligence techniques (hierarchical learningfeatures/learned representation of the features). All algorithms aredesigned to solve different tasks such as image enhancement, edgedetection, segmentation, registration, etc. With the help of Algorithmlayer 105, these tasks will be performed automatically, which willcontribute to the understating of the high-level complexity of medicaldata and also to the understanding of dependencies among the dataprovided in Data Layer. The algorithm layer 106 also includes learningalgorithms such as statistical models and prediction models.Representative examples include image quality scoring algorithm, DeepLearning algorithm, Machine Learning based algorithms, and imageregistration algorithms.

The computing platform 100 is configured to execute one or moreautomated artificial intelligence models. The one or more automatedartificial intelligence models are trained on data from the data layer105. The data layer 105 includes at least a plurality of surgicalimages. The artificial intelligence intra-operative surgical guidancesystem 1 includes a computing platform trained to calculateintra-operative surgical decision risks by applying an at least oneclassifier. More specifically the computing platform is trained toperform the classification of intra-operative medical images of implantsfixation into discrete categories that are predictive of surgicaloutcomes, for instance, optimal and sub-optimal. The automatedartificial intelligence models are trained to calculate intra-operativesurgical decision risks and to provide an intra-operative surgicalguidance, and a visual display configured to provide the intra-operativesurgical guidance to a user. The application layer 107 includes but isnot limited to: clinical decision support, surgical guidance, riskfactor and other post processing actions such as image interpretation aand visual display.

Now referring to FIGS. 3A & 3B example of automated artificialintelligence models are shown. The computing platform 100 is configuredto execute one or more automated artificial intelligence models. Theseone or more automated artificial intelligence models are trained on datafrom a data layer 105.

These automated artificial intelligence models include: Deep Learning,machine learning and reinforcement learning based techniques. Forexample, a Convolutional Neural Network (CNN) is trained usingannotated/labeled images which include good and bad images to learnlocal image features linked to low-resolution, presence ofnoise/artifact, contrast/lighting conditions, etc. The CNN model usesthe learning features to make predictions about a new image. The CNNmodel can include a number of conventional layers and a number ofpooling layers which proceed to subsampling (or down sampling) of eachfeature map while retaining the most informative feature. The stack ofthe layers can include various Conventional Kernels of size N×M; N and Mare positive integers and stand respectively for Kernel width andheight.

FIG. 3A illustrates a deep learning model architecture. The AI deep CCNarchitecture shown in FIG. 3A is for the classification for surgicaloutcomes (optimal vs suboptimal) with a score quantifying thisclassification using Convolutional Neural Networks. This architecturecomprises several (deep) layers involving linear and non-linearlearnable operators which enable building high-level information makingthe process of construction of discriminative information automated. Thefirst input layer of the deep learning network learns how to reconstructthe original dataset which is the collection of data layer 105. Thesubsequent hidden layers learn how to reconstruct the probabilitydistributions of the activations of the previous layer. The number ofthe hidden layers define the depth level of the deep learningarchitecture. The output layer of a neural network is tied to theoverall task.

As illustrated in the figure FIG. 3A, the classification CNN provides anoutput probability score for surgical outcome. This score quantifies thequality of positioning of an implant and/or bone alignment. CNNhyperparameters including the number of layers as well as the filtersizes were derived empirically upon testing the performance of thedesigned network on data collection from Data Layer. The CNNarchitecture is adjustable in a way to provide high sensitivitydetection for the positioning of the implant.

FIG. 3B is a schematic illustration of multi-scale reinforcement leaning(RL) as applied to the task of intraoperative screw insertion. This typeof reinforcement learning can intraoperatively illustrate insertiontrajectory and pedicle screw trajectories.

Now referring to FIGS. 4A-4B, the software is organized into modules andeach module has at least one function block as shown in this figure. Thenon-transitory computer-readable storage medium is coupled to amicroprocessor, wherein the non-transitory computer-readable storagemedium is encoded with computer-readable instructions that implementfunctionalities of the following modules, wherein the computer-readableinstructions are executed by a microprocessor.

The computing platform 100 of the artificial intelligenceintra-operative surgical guidance system 1 can include the followingmodules. Module 5 is made of an image quality scoring algorithm toassess the quality of an acquired medical image for its intended use.The image quality scoring algorithm is an image processing algorithmthat is based on Machine Learning or Deep Learning from a good and badmedical image training dataset for a specific application. For MachineLearning based algorithms, image quality score of a given image iscomputed based on quality metrics which quantify the level of accuracyin which a weighted combination of technical image factors (e.g.,brightness, sharpness, etc.) relate to how clearly and accurately theimage captures the original anatomical structures of an image. Theseweighed combinations of factors are known predictors of optimal orsub-optimal outcomes or performance measures. Examples: “adequacy ofreduction” (FIG. 28B). The weighted combination of technical factors isa parametrized combination of key elements which quantify how good theimage is. It can be seen as an indicator of relevancy of the image, anddetermines if the acquired image is sufficient to work with or not. Inthis invention, it is used to define the quality metric/image score

For Deep Learning based techniques, a Convolutional Neural Network (CNN)is trained using annotated/labeled images which include good and badimages to learn local image features linked to low-resolution, presenceof noise/artifact, contrast/lighting conditions, etc. The CNN model usesthe learning features to predict, for a new image, its image qualityscore.

As can be seen in FIG. 3A, the CNN model can include a number ofconventional layers and a number of pooling layers which proceed tosubsampling or down sampling of each feature map while retaining themost informative feature. The stack of the layers can include variousConventional Kernels of size N×M; N and M are positive integers andstand respectively for Kernel width and height. Module 5 maximizes theperformance of further computer vision and image processing tasks.Module 5 can also include a grid-based pose guide to assist the user inacquisition of a better image as appropriate to the application.

Module 6 includes one or more algorithms to detect and correct fordistortion inherent in medical imaging modalities, for example thefluoroscopic distortion inherent in intraoperative C-arm imaging.

Module 7 is an image annotation module that includes image processingalgorithms or advanced Deep Learning based techniques for detectinganatomical landmarks in a medical image and identifying contours orboundaries of anatomical objects in a medical image, such as bone orsoft tissue boundaries. Anatomical Landmark detection stands for theidentification of key elements of an anatomical body part thatpotentially have a high level of similarity with the same anatomicalbody part of other patients. The Deep Learning algorithm encompassesvarious conventional layers and its final output layer providesself-driven data, including, but not limited to, the system coordinatesof important points in the image. In the current invention, landmarkdetection can be also applied to determine some key positions ofanatomical parts in the body, for example, left/right of the femur, andleft/right of the shoulder. The Deep Neural Network output is theannotated positions of these anatomical parts. In this case, the DeepLearning algorithm uses a training dataset which needs to meet somerequirements: the first landmark in the first image used in the trainingmust be consistent across different images in the training dataset.Identifying contours of anatomical objects refers to providing an edgemap consisting of rich hierarchical features of an image whilepreserving anatomical structure boundaries using Deep Learningtechniques. A variety of highly configurable Deep Learning architectureswith an optimized hyperparameters tuning are used to help with solvingspecific tasks. The trained Conventional Neural Network in oneembodiment includes tuned hyperparameters stored in one or manyprocessor-readable storage mediums and/or in the Cloud ComputingEnvironment 98.

Module 8 is a preoperative image database including computer algorithmsand data structures for storage and retrieval of preoperative medicalimages, including any metadata associated with these images and theability to query those metadata. Preoperative images can includemultiple imaging modalities such as X-ray, fluoroscopy, ultrasound,computed tomography, terahertz imaging, or magnetic resonance imagingand can include imagery of the nonoperative, or contralateral, side of apatient's anatomy.

Module 9 is the Image Registration which includes one or more imageregistration algorithms.

Module 10 is composed of computer algorithms and data structures for thereconstruction and fitting of three-dimensional (3D) statistical modelsof anatomical shape to intraoperative two-dimensional orthree-dimensional image data. Module 11 is composed of image processingalgorithms and data structures for composing multiple medical images,image annotations, and alignment grids into image-based visualizationsfor surgical guidance.

Module 12 is an Artificial Intelligence Engine that is composed of imageprocessing algorithms based on Machine and/or Deep Learning techniquesfor the classification of intraoperative medical images of reduction andalignment procedures into discrete categories that are predictive ofdiffering surgical outcomes, such as suboptimal or optimal outcomes.Classifications produced by Module 12 can also include an associatedscore that indicates a statistical likelihood of the classification andis derived from the model underlying the image classifier algorithm, i.ea classifier.

Module 13 is an Artificial Intelligence Engine that is made of imageprocessing algorithms which uses Machine Learning or Deep Learningmethods for the classification of intraoperative medical images ofimplant fixation procedures into discrete categories that are predictiveof differing surgical outcomes, such as suboptimal or optimal.Classifications produced by Module 13 can also include an associatedscore that indicates a statistical likelihood of the classification andis derived from the model underlying the image classifier algorithm.

Module 14 is a postoperative image database made of computer algorithmsand data structures for storage and retrieval of postoperative medicalimages, including any metadata associated with these images and theability to query those metadata. Postoperative images can include imagesacquired during routine follow-up clinic visits or surgical revisions.

Module 15 is an Artificial Intelligence Engine that is made of imageprocessing algorithms for the classification of a time series ofpostoperative medical images into discrete categories that arepredictive of differing surgical outcomes, such as suboptimal or optimaloutcomes. Classifications produced by Module 15 can also include anassociated score that indicates a statistical likelihood of theclassification and is derived from the model underlying the imageclassifier algorithm.

Module 16 is a fracture identification and reduction module with accessto an AO/OTA Classification Dataset interprets the image and makes aclassification of the bone, bone section, type and group of thefracture.

Now referring to FIG. 2B, the computing platform 100, which includes oneor more Artificial Intelligence (AI) Engines, including FIG. 4A, Modules12, 13, and 15, and information from a series of datasets. Here deepneural networks and other image classifiers are trained to analyze andinterpret visual features in one or more images to anticipate problemsand predict outcomes in a surgery or in a postoperative follow-upperiod. Training relies on one or more medical image datasets withassociated known outcomes data. A trained neural network in this contextcan thus be thought of as a predictive model that produces a surgicaloutcome classification from an input set of medical image features.

The outcome classification is typically also accompanied by astatistical likelihood that the classification is correct. Together, theclassification and its likelihood can be thought of as an outcomeprediction and a confidence level of that prediction, respectively. Inthe case of a suboptimal outcome prediction, we can consider theconfidence level to be a Failure Risk Score for a suboptimal outcome.The classification and Failure Risk Score can thus be used by thesurgeon to support decisions that lead to optimal outcomes and avoidsuboptimal outcomes. Any number of classical machine learning approachescan be used, as well as more modern Deep Learning networks [LeCun, Yann,Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553(2015): 436], such as Convolutional Neural Networks [e.g. Lawrence,Steve, et al. “Face recognition: A convolutional neural-networkapproach.” IEEE transactions on neural networks 8.1 (1997): 98-113.] Asurgical outcomes classifier with a confidence score can also beconstructed using any number of methods in multivariate statistics,including a Cox proportional hazards model or other commonregression-based time-to-failure models constructed from clinicalresearch data. In the case of a classifier constructed using amultivariate statistical model, the inputs include at least in partfeature sets derived from the medical image data. For instance, in orderto identify surgical outcomes using “non-image” datasets, for examplediagnosis reports derived from datasets (e.g. “Dataset Outcomes SurgicalVariables” in FIG. 4B), Natural Language Processing (NPL) can be used toprocess clinical text data.

The systems and methods describe uses for the artificial intelligentplatform, such as the ability to read and interpret subject image data,and calculate surgical decision risks, and provide the end user with aconfidence score of the probability of an optimal outcome and predictorof performance metrics for implants and surgical factors. This occurs bydynamically updating, by the computing platform, the composite imagewith the at least one surgical guidance.

The computing platform 100, which includes an artificial intelligenceengine, utilizes and analyzes the information from the datasets. Theseinformation sets have been analyzed and structured and based upon thespecific surgical application can include: procedural medical imagedatasets, such as intraoperative fluoroscopic images and pre- andpostoperative x-ray, MRI or computed tomography data; an AO/OTADanis-Weber fracture classification dataset; Lauge-Hansen classificationsystem dataset; implant 3D CAD model datasets, biomechanical testingsuch as Von Mises Stresses failure modes datasets; medical image featuredatasets and learned models for anatomical feature tracking; best-posegrid datasets; fracture reduction image datasets with associatedoutcomes data; other surgical outcomes datasets: peer-reviewedliterature and clinical studies datasets; known predictors andindicators of complications datasets; 3D statistical models of humananatomy datasets; other medical image datasets; an expert physiciandomain knowledge datasets; bone quality index datasets; failure riskscore datasets; subject HER information data; and outcomes surgicalvariables datasets such as trauma outcomes data, arthroplasty outcomesscoring data, ACL outcome rating scales, and spine scoring systems.

In addition to these surgical and procedure specific datasets,information from subject health records such as comorbidity data, thepresence of deformity, and bone quality index scores can be accessed.These datasets are configured to include information that willpotentially have an impact on the outcome of the procedure. The datasetsare used to interpret critical failure mode factors of an implant oranatomical alignment and when used to train an outcomes classifier foran Artificial Intelligence Engine provides the user with a prediction ofoptimal or suboptimal outcome and an associated Failure Risk Score. TheAI engine include multiple CNNs based classifiers which can be selectedusing the specific dataset (one or more dataset, most importantlyuncorrelated data that make the CNN learn new relevant features) fromData Layer for solving a well-defined task, for example, determine theposition of implants, etc.

The information from independent datasets can be accessed at any giventime, or alternatively a situation during the event can require theinput from various datasets simultaneously. In this situationinformation from the relevant datasets will be selected for inclusion inthe Artificial Intelligence (AI) Engine in the form of multiple trainedclassifiers, each with a weighted contribution to the final surgicaloutcome prediction. In this case, Machine and/or Deep Learningtechniques are intended to identify relevant image features from inputspace of these datasets and the AI Engine seeks an individual customizedsoftware solution to a specific task, for example a decision regardingimplant positioning or surgical guidance, and datasets involved to solvethat task. This multiple prediction model utilizes information fromdatasets that have a relationship from the perspective of sharinguncorrelated or partially correlated predictors of a specific outcome.The AI Engine can further weight the outcome prediction data based uponthe relative level of criticality regarding performance or failure. Themodel outputs decision support and outcome predictions for example theprobability of a successful and optimal long-term outcome.

The computing platform 100 is configured to synchronize with a ComputerAssisted Surgery (CAS) system to provide the same predictive guidance asdescribed throughout as an enabler for computer assisted surgery. Thedynamic surgical guidance system 1 described herein, has the capabilityto provide predictive guidance or act as an enabler for subjectspecific, or custom, matched-block guided technology. For example, thepresent invention can be applicable to other musculoskeletalapplications such as arthroplasty surgery for hip, knee, ankle andshoulder as well as trauma surgery for musculoskeletal repair and forspine applications. Typical applications include hip, knee, shoulder,elbow, and ankle arthroplasty, trauma fractures and limb deformitycorrection, spine, and sports medicine procedures such asfemoroacetabular impingement/(FAI)/Periacetabular Osteotomy (PAO). Theartificial intelligence intra-operative surgical guidance system 1 isconfigured to implement a method including the steps of: obtainingsubject image data; dynamically displaying the subject image data on agraphical user interface; selecting an anatomical structure within thesubject image data and mapping a grid template to the anatomicalstructure to provide a registered image data; providing an artificialintelligence engine and at least one dataset configured to generatesurgical guidance; providing as a data output, the registered imagedata, to the artificial intelligence engine to generate at least onesurgical guidance; and dynamically updating, by the computing platform,the composite image of the registered image data with the at least onesurgical guidance. The surgical guidance is related to: deformitycorrection, an anatomy alignment, a fracture reduction and an anatomyreduction. The process of surgical guidance will be discussed in thefollowing section for these applications. The method further includesthe step of generating a trackable location and orientation guided bythe grid template. These steps will be more fully described as they areimplemented in FIGS. 5A-31 .

Now referring to FIGS. 4A, & 5A-5C, an over image of the use of anartificial intelligence engine and the data sets applied to registeredpre-, intra-, and postoperative images yields a graphical user interfacefor use in reduction and alignment and implant fixation procedures. Thepreoperative workflow is provided. The workflow proceeds as follows. Theimaging system 110 of the artificial intelligence intra-operativesurgical guidance system 1 receives subject image data such as one ormore preoperative images and computes an image quality score using ImageQuality Scoring and Pose Guide Module 5. The user is presented with achoice to either accept or reject the image based on the image qualityscore and pose guide guidance. If the image is rejected, the operatortries again to acquire an acceptable image. If accepted, distortion inthe image is detected and corrected using the Distortion CorrectionModule 6. The image is then annotated with anatomical landmarks andimage segmentations using the Image Annotation Module 7. Images are thenstored for later use in the intraoperative and postoperative workflowsvia the Preoperative Image Database Module 8. The process is thenrepeated to acquire any number of images necessary for later referencein the intraoperative and postoperative workflows.

Now referring to FIGS. 4A & 5B, the intraoperative workflow is provided.The process proceeds as follows. The artificial intelligenceintra-operative surgical guidance system 1 receives one or morepreoperative images and computes an image quality score using ImageQuality Scoring and Pose guide Module 5. The user is presented with achoice to either accept or reject the image based on the image qualityscore and pose guide guidance. If the image is rejected, the operatortries again to acquire an acceptable image. If accepted, distortion inthe image is detected and corrected using the Distortion CorrectionModule (6). The image is then annotated with anatomical landmarks andimage segmentations using the Image Annotation Module 7. The artificialintelligence intra-operative surgical guidance system 1 then registersthe image to the best matching corresponding image in Preoperative ImageDatabase module 8 and computes a matching score using the ImageRegistration Module 9. The user accepts or rejects the image andregistration based on the registration match and quality score. Ifaccepted, three-dimensional anatomical shape information can be computedusing the 3D Shape Modeling Module 10 followed by a registration(mapping) of an alignment grid to annotated image using the ImageRegistration Module 9.

The step of registration is the process of transforming images ofpreoperative of nonoperative side (the fixed image, f(x),) andintraoperative of the operative side (the current moving image, m(x),)to a common coordinate system so that corresponding pixels representhomologous biological points. This means recovering the transform, T(x),which maps points in f(x) to m(x). This is accomplished by the steps of:(1) define the transformation model, (2) determine the similaritymetrics describing the objective function to be minimized, and (3) theoptimization algorithm that solves the minimization problem. Theeffective alignment of these images will allow the surgeon to highlightdifferent characteristics and therefore establish a better comparison ofthese images. It should be noted that the images that are registered donot have to be imaged from the same modality; it can be MRI to CT or CTto CT, and so on.

The computing platform 100 of the artificial intelligenceintra-operative surgical guidance system 1 produces a composite image orimages for display to the user using any combination of the currentacquired image, the aligned preoperative image, the registered alignmentgrid using the Image Composition Module 11. Here different processes arefollowed depending on the type of procedure. For reduction & alignment,the system computes an outcome classification and Failure Risk Scoreusing the Reduction and Alignment Outcomes Prediction Module 12. Forimplant fixation, the system computes an outcome classification andFailure Risk Score using the Implant Fixation Outcomes Prediction Module13.

The artificial intelligence intra-operative surgical guidance system 1then annotates the displayed composite image and graphical userinterface with the outcome classification and Failure Risk Score, alongwith any surgical guidance information. Surgical guidance directives canthen be communicated to a surgical facilitator 160 such as a hapticfeedback device, a robot, a trackable guide such as tracked Implant orobject, a cutting block, a computer assisted surgery device, IoT deviceand a mixed reality device.

Now referring to FIGS. 4A & 5C, the postoperative workflow is provided.The process proceeds as follows. The artificial intelligenceintra-operative surgical guidance system 1 receives one or morepostoperative images and computes an image quality score using ImageQuality Scoring and Pose guide Module (5). The user is presented with achoice to either accept or reject the image based on the image qualityscore and pose guide guidance. If the image is rejected, the operatortries again to acquire an acceptable image. If accepted, distortion inthe image is detected and corrected using the Distortion CorrectionModule 6. The image is then annotated with anatomical landmarks andimage segmentations using the Image Annotation Module 7. The artificialintelligence intra-operative surgical guidance system 1 then registersthe image to all preceding time series images in the Postoperative ImageDatabase 14 and computes matching scores using the Image RegistrationModule 9. The user accepts or rejects the image and registration basedon the registration match and quality score. If accepted,three-dimensional anatomical shape information can be computed using the3D Shape Modeling Module 10, followed by a registration (mapping) of analignment grid to annotated image using the Image Registration Module 9.

The artificial intelligence intra-operative surgical guidance system 1produces a composite image or images for display to the user using anycombination of the current acquired image, the aligned preoperativeimage, the registered alignment grid using the Image Composition Module11. The system then computes an outcome classification and Failure RiskScore using the Postoperative Outcomes Prediction Module 13. Theartificial intelligence intra-operative surgical guidance system 1 thenannotates the displayed composite image and graphical user interfacewith the outcome classification and Failure Risk Score, along with anyguidance information.

Now referring to FIG. 6 , the subject is prepared and positioned for amedical or surgical event in a standard manner as indicated for thespecific procedure, for example, joint replacement, orthopedic trauma,deformity correction, sports medicine, and spine. The preoperative image115 or data is imported and is shown as FIG. 6 . The preoperative image115 shows a grid template 200 super imposed over the subject'sanatomical image.

A grid template 200 has a plurality of dimensioned radio-opaque lines,e.g. 230 relating to surgical variables. The portion of the gridtemplate 200 that is not opaque is radiolucent. The grid template 200can include any shape or pattern of geometric nature or text toreference angles, length positioning or targeting. The grid template 200can be a single line, a geometrical pattern, number, letter or a complexpattern of multiple lines and geometries that correspond to surgicalvariables. The grid patterns can be predesigned or constructedintraoperatively in real-time based upon the surgeon's knowledge ofanatomy and clinical experience including interpretation of morphometricliterature and studies identifying key relationships and dimensionsbetween anatomical landmarks and its application in supporting goodsurgical technique as it relates to specific procedures. With respect toa digital dimensioned grid, this form of the grid template 200 isgenerated by the application software.

The subject is prepared and positioned for a medical or surgical eventin a standard manner as indicated for the specific procedure, forexample, joint replacement, orthopedic trauma, deformity correction,sports medicine, and spine. The procedure specific information for therespective application is extracted from the preoperative image 115 ordata and mapped into live intraoperative images 120. Mapping is definedas computing a best-fit image transformation from the preoperative tothe intraoperative image space. The transformation is made of thecomposition of a deformation field and an affine or rigidtransformation. The best fit transformation is computed using a varietyof established methods, including gradient descent on mutualinformation, cross-correlation, or the identification of correspondingspecific anatomical landmarks in preoperative 115 and intraoperativeimages 120. See, e.g. U.S. Pat. No. 9,610,134 specifically incorporatedby reference in its entirety.

Now referring to FIGS. 7A, a new image 120 of the unaffected anatomy isacquired and transmitted (can be wirelessly) to the computing platform100. At the beginning of the procedure, the user will use the softwareof the computing platform 100 to assist with anatomical positioning,namely the Image QS and Pose module as shown in FIG. 4A. The computingplatform 100 identifies landmarks on the intraoperative image 120 anddetermine an optimal pose for the image to be taken. Landmarks areidentified based on classifiers learned from the medical image datasets.Outcome classifiers can take the form of a deep neural network, atemplate matching algorithm, or a rule-based classifier or decisiontree.

Now referring to FIG. 7B, the is computing platform 100 provides areal-time guide template 250 of good pose estimation. The guide template250 is a guide for the user to acquire a best-fit opportunity for theartificial intelligence intra-operative surgical guidance system 1 tomatch subsequent new images 120 with the dataset of optimal-pose-imagesas shown on an electronic display device 150. For example, in an ankle,once a pose is selected, the lateral image can be segmented intospecific anatomical features and a guide template 250 mapped to thesefeatures—tibia, fibula, and talus.

FIGS. 8A-C shows the overlay mapping of images and pose guide template250 to provide a pose-guide image 260. Once the user has acquired thepreferred or correct image pose, then that image and/or guide template250 can be used as the guidance pose-guide image 260 for the remainderof the procedure. The guidance pose-guide image 260 can be that of acontralateral or unaffected side of the body, or a best-fit image from adataset, or a statistical shape model, or a geometric virtual grid.

The user takes subsequent images until satisfied it matched the guidancepose-guide image 260 required or a threshold is detected. The correctpose can be acquired in two ways, 1) by adjusting position of anatomy(subject), or 2) by adjusting pose/angle of imaging equipment (exC-arm). This process can be manually instructed by the user orautonomously performed by the software module of the computing platform100. The computing platform 100 attempts to determine whether thematched image 270 is a good match for one of the images 120 in thepreoperative image database. Here the computing platform 100 uses theImage Registration Module 9 as shown in FIG. 3A.

This process involves a multi-scale image-based registration metric thatcan be quickly applied to the image pairs. If a match above a thresholdis detected, the computing platform 100 attempts to automaticallyidentify relevant anatomical landmarks in the new image using any of thetechniques for image landmark classifiers. Landmark information andoptionally other image information is used to compute a transformation Tof the new image to the coordinate space of the preoperative image.

Now referring to FIG. 8B, auto landmarking and smart image registrationoccur at this time whereby the computing platform 100 attempts toautomatically identify relevant anatomical landmarks, such as posteriordistal fibula 271, in the matched image 270. Any of the aforementionedtechniques for landmark classifiers can be used. In addition, image nailapproaches based on edge and feature recognition, landmarkidentification, statistical atlas registration, and deformable modelscan be used to segment relevant areas of anatomy from the image, such asthe talus 272. In these images, the matched image 270 is a template thatacts a pose guide 250 in this situation.

Now referring to FIG. 8C the best image pose guidance good side withimage registration module 9 and pose guide module 5 in addition toexpert domain knowledge dataset is accessed as shown. Once the best poseimage is accepted, the image registration module 9 identifies theapplication specific feature(s) 280, for example the talus in an anklefracture procedure. The output is an automatic identification andselection of the desired anatomical features and a grid template 200positioned relative to these features 280 displaying image and gridalignment.

Now referring to FIGS. 4A and 5 , in the image registration module 9, auser such as a surgeon, selects at least one anatomical landmark in theanatomical image on the graphical user interface 151. Anatomicallandmark selection can be accomplished by a various methods includingbut not limited to: auto-segmentation where the software of thecomputing platform 100 uses feature/pattern recognition process toauto-detect known and targeted anatomical landmarks; use of a remoteinfrared device, such as a gyro mouse; voice command; air gestures; gaze(surgeon uses gaze and direction of eye or head motion to controltargeting) or touching the visualization screen at the selectedanatomical landmarks.

In one illustrative embedment, the surgeon inputs the selection of theanatomical landmarks to the workstation manually or using a variety ofinput devices such as, an infrared wand or an augmented reality device.The application software of the computing platform 100 registers a gridtemplate 200 with the selected anatomical landmarks. The method includesthe step of registering an anatomical image to a grid template 200 byselecting at least one anatomical landmark to provide a grid template200 with at least one grid indicator 280. A grid indicator 280 is ananatomical feature defined and located on an image that correlates witha known position on a virtual grid template 200 for purposes ofregistration. If needed a registration procedure is used to eitherunwarp the image or warp the digital grid indicators according to theimage warping.

The software of the computing platform 100 identifies and recognizescalibration points that are radiopaque in the image. These are of knowndimensioned geometries. A grouping of these points is a distortioncalibration array. The distortion calibration array is placed on theimage intensifier or in the field of image of any imaging system so thatthe known distortion calibration array lines/points are identified whenan image is taken and captured. These known patterns are saved for usein the distortion adaptation/correction process. The distortioncalibration array is removed from visualization on the display medium tonot obscure and clutter the image with unnecessary lines/points. Adistortion calibration array can be made a series of lines or pointsthat are placed to support the distortion adaptation of the gridtemplate 200. The distortion calibration array points or lines areradiopaque so that the distortion process can calculate the location ofthese points/lines relative to the anatomy and quantify the amount ofdistortion during each image taken. Once these points/lines areidentified and used in the distortion process, there is another processthat removes the visualization of these points/lines from the anatomicalimage so that they are not obstructing the surgeon's image when he orshe sees the grid template 200 and the anatomical image.

In one embodiment, the registration process involves manually orautomatically detecting grid landmarks (such as grid line intersections,points, and line segments) on the grid template 200 superimposed on theanatomical image and then aligning those landmarks via an AffineRegistration and a deformation field with corresponding landmarks on adistortion calibration array of known geometry, which is a representeddigitally. The method includes the step of deforming the calibrateddimensioned grid to correct for the distortion of the anatomical imageto generate a deformed calibrated dimensioned grid image. Knownradiopaque lines/points (from distortion calibration array) are used toprovide a measure of EM distortion in each anatomical image. Thedistortion is quantified and then the software of the computing platform100 generated virtual grid is adapted to match the distorted anatomy ineach anatomical image.

The distortion calibration array is of non-uniform design, such that theselected anatomical landmarks are clustered more densely in regions ofinterest to the surgeon, in order that the deformation correction can beestimated with greater precision in those regions. The deformationestimation proceeds as follows: once selected anatomical landmarks havebeen identified (either manually or automatically) on the array image,an Affine Transformation that produces the best mapping betweencorresponding selected anatomical landmarks from the grid template 200to the array image is computed. Following transformation of the gridpoints by the Affine Transformation, which adjusts the landmarks fortranslation, rotation, and scaling with respect to the array imagelandmarks in the Deformation Field (which is the residual differencebetween transformed grid points and the array image points) is modeledusing Thin-Plate Splines or any other suitable radial basis functions.Parameters of the Thin-Plate Splines or radial basis functions areestimated by solving a linear system of equations. U.S. patentapplication Ser. No. 15/383,975 (hereby specifically incorporated byreference). The array image becomes the reference image or thecalibrated image.

Once the deformation field has been computed, the dimensioned grid isadapted in real-time intraoperatively to fit the subject anatomy, thusproducing a distorted grid indicator, such as lines curving that can beused to match or fit the musculoskeletal anatomy or the shape of theimplant. The deformation of the grid indicators is then applied inreal-time by first applying the Affine Transformation and then warpingthe grid indicators along the Deformation Field. A grid pattern basedupon the anatomical points that was defined and targeted in landmarkidentification is generated. The software of the computing platform 100is configured to compute the amount of distortion in each image and itquantifies this amount relative to the anatomical image and thendisplays the calculated grid/Image relationship displaying an image ofthe subject's anatomy with the quantitatively distorted dimensioned gridimage. These deformed grids are tracked in real time with each new imagetaken. The deformed grid can be positioned relative to anatomy, implant,and fractures auto or manually by the user such as a surgeon. Numerousequations and formulas are used within the algorithms to calculate:measurements, differences, angles, grid and implant positions, fracturedeviations to determine at least one measurement of surgical variablesinvolving the implant or trauma.

In auto-segmentation, the at least one anatomical landmark selected bythe surgeon is automatically selected for each successive anatomicalimage. Auto-segmentation allows a surgeon to work more rapidly.Auto-segmentation is accomplished through a combination of one or moreof the following techniques: intensity thresholding; feature detectionon the intensity edges in the image, including shape detection via theHough Transform or other methods; feature detection followed byregistration of a 2D or 3D anatomical atlas with predefined landmarkpositions.

Now referring to FIGS. 9A-B and 10, an intraoperative image withanatomical features defined is shown as a graphical user interface. Agrid template 200 is mapped to the identified anatomical object orimplant feature to form a matched grid map image 290. In this image theobject is a lateral talus 280. The computing platform 100 is configuredto auto track features and calculate new coordinate positions andintegrated grid map of anatomy, objects, and implants. The graphicaluser interface is i) locked for auto tracking of subsequent images and,ii) auto integrate and position an alignment grid. Using the graphicaluser interface, an alignment grid 350 is positioned in the matchedimage. The user adjustments are accepted and the computing platform 100is configured to provide a matched grid map 290.

Now referring to FIGS. 11A-B, an image 121 of the affected side isobtained. The affected side could show a deformed, injured or diseasedlimb. The process previously described for the good side is repeated forthe affected side. In this process, the best pose and auto identifyanatomical features, objects and implants occurs by matching to the goodside of the subject. The inputs to the process are: affected side image,good-side pose guide data and feature identification of the data set.The task conducted by the computing platform 100 is to estimate the bestpose match with the good side and auto map the feature and the grid map.The action required is calculate the best pose, auto identify featuresand calculate the matched grid map. The output is the good side(contralateral) and afforded side are matched with the alignment grid ispositioned on the image.

Now referring to FIGS. 12A-B, a fracture identification and reductionmodule 16 with access to an AO/OTA Classification Dataset interprets theimage and makes a classification of the bone, bone section, type andgroup of the fracture. As shown in FIG. 12A as a graphical userinterface, the user then can accept the classification. In an exemplaryembodiment, in the event that the procedure involves a fracture of thebone, the affected side image is analyzed for a fracture and the type offracture classified 45 according to the AO/OTA classification system.For example, an ankle fracture selection classified as metaphysealcomplex (43-A3). Next if the classification is accepted, then the taskand actions include providing the treatment option 47.

As shown in FIGS. 12 B-C, a treatment option 47 is provided based on theclassification selected. Treatment option 47 is shown as a graphicaluser interface. Here a trauma plate implant 49 is shown for use in thisfracture situation. This is accomplished by the computing platform 100configured to acknowledge the selection of fracture type from therelevant dataset, and algorithmic modules are accessed for a treatmentplan analysis and suggestion for the user 155. The treatment option 47will include a determination of the recommended implants for fixation ofthis type of fracture classification, for example an implant plate withspecific screw configurations and selections 49. Automatic fractureidentification can be accomplished using a classifier trained withvarious machine learning approaches, including deep convolutional neuralnetworks or rule-based classifiers.

More specifically, the CNN model is trained on datasets which includeimages with one or more fractures and other images without fractures.Then, the CNN model determines whether there is a fracture or not andalso localizes the region of interest which contains the identifiedfracture and/or the abnormality. Precise identification of the fracturearea in the image is critical information required to support theclassification, in addition to providing evidence regarding the fixationprocess for the type of fracture. In practice, the input image isprocessed by a CNN model (representative architecture is illustrated inFIG. 3A, which includes various conventional and max-pooling layers, inorder to produce feature maps. At the last layer, the network ismodified to target a candidate ‘fracture region of interest’ on theimage based upon feature map information giving the location and size ofthe region of interest. Candidate regions, including all fracturelocation and suspected abnormality detections, marked by the CNN modelare then shown.

Now referring to FIGS. 13A-B, a guidance pose-guide image 260 can alsobe used as the good-side reference image to be used throughout theprocedure as a template image for the similarity evaluation and mappingmodule whereby the anatomy of the good, or unaffected, side is matchedwith the image of the affected side anatomy 121. This is accomplished bythe computing platform 100 configured to use image analysis andsegmentation techniques to autonomously perform a similarity evaluationto identify and register bony anatomical features, in real-time, of theaffected and contralateral images. This technique is identified asghosting. In ghosting, an overlay image 50 is obtained by overlaying thegood-side image 120 versus the bad side image 121 with reference to theguidance pose-guide image 260. Matching of the operative andcontralateral-side images is accomplished by computing a best-fit imagetransformation. The transformation can include the composition of adeformation field and an affine or rigid transformation. The best fittransformation can be computed using a variety of established methods,including gradient descent on mutual information, cross-correlation, orthe identification of corresponding specific anatomical landmarks inpreoperative and intraoperative images.

More specifically, as shown in FIGS. 5A-5C, the ghosting procedureinvolves the steps of: 1) Before the surgery, operating room personnelacquires preoperative image(s) of the nonoperative side of the patient'sor subject's anatomy. For example, in an ankle fracture case, standardanterior-posterior, lateral, and mortise images of the uninjured anklewould be taken. These images might also be ordered by a surgeon to beacquired by radiology using standard x-ray before the case and thenloaded onto our device before the surgery. 2) The nonoperative-sideimages acquired in step 1 are processed by the computing platform 100 toidentify key landmarks that will later be used for image registrationwith the operative side. Additionally, these images may be corrected fordistortion and may have a grid template overlaid. 3) During thereduction phase of the surgery, images of the operative side areacquired that the surgeon uses for reduction of the fractured anatomy.These operative side images are processed to identify key landmarks thatwill be used for image registration with the nonoperative side.Additionally, these images may be corrected for distortion. 4) Thecomputing platform 100 identifies the best-matching nonoperative sideimage to the current operative side image using an image similaritymetric. The best-matching nonoperative side image is registered to thecurrent operative side image. The registration process computes an imagetransformation with is made of a transformation matrix (affine orrigid), a deformation grid, or both. 5) The computing platform 100 usesthe image transformation to align the non-operative-side image with thecurrent operative-side image and produce an image overlay thatillustrates the difference in the anatomical positioning of thenon-operative and operative-side images. This overlay image is aguidance pose-guide image 260 that is a template that the surgeon canuse to restore the patient's normal anatomy on the operative side (basedon the non-operative side anatomical positioning). 6) Any dimensionedgrids or other annotations placed on the non-operative side image can betransformed to their corresponding position on the operative-side imageto augment the registered composite image.

Now referring to FIG. 14A, a similarity evaluation 52 is performed andthe registration match confidence is calculated based in the ImageRegistration Module 9, which includes one or more image registrationalgorithms. The similarity evaluation 52 shows similarity evaluation andmapping.

In FIGS. 14B-C, the grid similarity mapping of the confidence percentageis shown in a graphical user interface display by aligning thenon-operative-side image with the current operative side. A renderingdisplay of the ‘ghosting’ or good vs bad mapping/matching/overlay ofdifference 53 that has been calculated by the computing platform 100.The key color is the red showing the area difference between the twoimages. Using the highlighted guide, the match measurement 54 are shownto the user to accept or decline the information provided. In the userconfirms good-side acceptable for use in for example alignment.

In FIGS. 15A-C, a graphical user interface displays the matchmeasurement 54 for the user to accept or decline the informationprovided. In an exemplary example, a fibula length match of −5.5 mm isshown. In FIG. 15 B, an example of a graphical user interface displayshows an ankle. The good side is over-laid with the grid alignment andmeasurements. A ghosting interpretation of an ankle 55 is shown. FIG.15C is an example of a hip graphical user interface demonstrating aghosting interpretation of a hip 56 on the good side overlay with gridalignment and measurements for a nail example.

In FIG. 16 , the output of the Shape Modeling Module 10 is data forintraoperative calculation and guidance based on the 3D to 2Dregistration (or fitting) of a statistical shape model of theapplication anatomy to the 2D. A statistical shape model is used topredict the rotation in nailing applications. A statistical shape modelis a representation of the variability of anatomy in a population thatis encoded as one or more sample mean shapes plus modes of variability.A variety of image processing methods exist whereby a statistical shapemodel can be fit to images of patient anatomy in order to augmentmissing information in the medical image. For example, thethree-dimensional pose, including rotation, of a two-dimensionalfluoroscopic image of an implant such as an intertrochanteric nail canbe inferred using a statistical model of implant nail geometries. Thepose inference is computed by a simultaneous fitting of the statisticalmodel to the two-dimensional image and an iterative registration of thetwo-dimensional image with simulated projects of the statistical modelof the anatomy.

Now referring to FIGS. 17A-B for example, the computing platform 100identifies a varus reduction 60 of the femoral head during a procedureand provides the user with a varus reduction warning 61 indicating ahigh risk of failure based upon the calculations of the weightedintelligence model. A grid template of an optimal reduction can beprovided to provide guidance and assist the user in obtaining asatisfactory outcome.

The computing platform 100 is configured to analyze and interpret theinformation and provide guidance based upon a correlation to a known setof patterns and inference from datasets as set out in FIGS. 4A & 4B. Theoutputs related to surgical guidance include implant selectionrecommendations, implant placement, performance predictions, probabilityof good outcomes, and failure risk scores. The structure of the deeplearning platform demonstrating the different layers is indicative ofthe configuration of the flow of information and how the information isapplied. The data layer is made of a collection of data from variousnetworks. In a trauma event, such as fracture reduction or deformitycorrection, or in an arthroplasty event such as hip or knee anatomicalalignment or bone cut guidance, or in the event of a spine procedurewith implant alignment correction, or in a sports medicine event withACL reconstruction alignment, these surgical and procedure specificdatasets coupled with domain knowledge and information from subjecthealth records that are critical to an event can be accessed. The dataset are used to interpret critical failure mode factors of an implant oranatomical alignment and combined will provide the user with a FailureRisk Score with the output to the user as a confidence percentagerecommendation of a suboptimal or optimal performance metric. The outputis presented to the user in the form of intelligent predictors andscores to support decisions encountered in a real time event.

The is computing platform 100 analyzes an image for risk factors thatthe user cannot see due to their human inability to interpret anoverwhelming amount of information at any specific moment. If theimplant placement and the alignment does not match this data pattern, itwill create an awareness in this specific situation and provide a hazardalert to the user. Essentially, identifying and predicting problemsahead of the user encountering them. This can lead to avoidance ofcomplications and prevention of errors. The surgical guidance is relatedto: deformity correction, an anatomy alignment, a fracture reduction andan anatomy reduction. The process of surgical guidance will be discussedin the following section for these applications. The following sectionsshow how the computing platform 100 interprets the information andprovides guidance based upon a correlation to a known set of patternsand inference from data sets as applied to different surgicalprocedures.

TRAUMA EXAMPLE—HIP FRACTURE. The most frequent fractures hospitalized inUS hospitals in 2015 were for those of the hip, according to data fromthe HCUP (Healthcare Cost and Utilization Project of the Agency forHealthcare Research and Quality (AHRQ)). There are known reasons for thefailure modes of these procedures. For example, it is documented thatdetermining and utilizing the correct entry point for nailing of a bonecan prevent malreduction of the fracture and ultimately failure of theimplant or the compromising of an optimal outcome.

Subject anatomy is unique and using a single-entry point for allsubjects is not desirable. Once the subject has been prepared forsurgery in a standard manner, the artificial intelligenceintra-operative surgical guidance system 1 is turned on and the platformis now activated. A new image of the subject's anatomy is taken. Theimage is of the contralateral unaffected, or good, side of the subject.The platform is instructed, or will determine, if the information itreceives is 3D or 2D. If the information is 3D, then the artificialintelligence intra-operative surgical guidance system 1 will call therelevant module to perform a 2D to 3D registration or utilize the 3Dmodel for statistical inference. If the image is 2D, the artificialintelligence intra-operative surgical guidance system 1 will capture theimage and an initial grid pose module will analyze the image anddetermine if the pose of the image is adequate for use as a ‘true’image. A true image is a datum or base image that will be utilizedthroughout the procedure as a good anatomical reference image that thesoftware algorithm is able to reproducibly recognize. With each newimage acquired during this step of the procedure, the algorithm willaccess a dataset of annotated good anatomy pose images and provide avirtual grid pose template to guide the user to establish the correctpose. An example of the grid pose in this situation would be to advisethe user to ‘internally rotate the hips 15 degrees in the AP image withthe beam centered at the teardrop’. Once the correct image is accepted,the computing platform 100 accessed the automated image segmentationalgorithm to identify the relevant anatomical features. In this specificapplication (FIG. 18A), the anatomical features to be identified caninclude the tip of the greater trochanter (GT) 70, femoral shaft axisidentified by center of canal 71, and the femoral neck axis identifiedby center of femoral head and center of neck 71. The neck-shaft angle 71is measured as the medial arc between the shaft and neck axes. Theaffected side image is now taken with a similar matching image pose. Thefeature recognition and segmentation algorithm are accessed as well. Theimage registration module 9 is accessed here and a good-side match modelis calculated. A good vs bad grid map similarity evaluation provides theuser with a match confidence percentage. This step involves: mapping agrid template to the anatomical structure to register an image for thenonoperative side of the subject's anatomy with an image of theintraoperative image of the operative side of the subject's anatomy toprovide a registered composite image. The registered composite image isprovided to the artificial intelligence engine to generate an at leastone graphical surgical guidance In this step, the implant dataset isaccessed. The dataset includes information on the three-dimensionalgeometry of the implant options. The machine learning algorithmassociated with this dataset analyzes, measures, and calculates therelevant variables and has the capability to identify suboptimal outputsand provide the user with situational awareness and hazard alertsleading to complication avoidance and error prevention. The output ispresented to the user as surgical guidance, such as an optimal orsub-optimal risk score for failure if the user proceeds with the pathwayalong which he intends follow.

The computing platform 100 predicts performance based upon thesepathways and can also provide the user with a probability of an optimalor sub-optimal outcome. The computing platform 100 provides the userwith an implant recommendation based upon the known predictors of asuccessful outcome. The computing platform 100 dynamically updates theregistered composite image with the at least one graphical surgicalguidance as the surgeon changes interoperative variables. The surgicalvariable depends upon the ongoing surgery and includes the position ofthe patient, the pose estimation, the implant position or the nail entrypoint in a femur fracture surgical procedure.

Now referring to FIGS. 1A, 4A &. 18B, the correct nail entry-siteprediction information is now accessed within the known indicators andpredictors of complications dataset. The dataset uses information fromindustry gold-standard or historical peer-literature reviewed studies toperform analytical calculations and determine optimal and suboptimalperformance predictions. In addition, the computing platform 100determines if there is a probability of an error occurring by a specificplacement of an entry-site or orientation of a guidewire or reamer 75.The optimal entry point prediction utilizes a combination of the variousalgorithmic modules for information.

In this application, the relevant landmarks are identified using thesegmentation machine learning algorithm, the implant dataset is used forsubject best entry-point grid templating, the nail grid template isoptimally auto-placed on the image using the deep learning algorithmwith access to the known-literature and study dataset, the distancesbetween the various features are measured and the output values willpredict an outcome using the literature and study dataset with knownoptimal versus suboptimal risk values.

Now referring to FIG. 18C, for example, the user can want to use anentry-point at the GT (Greater Trochanter of Femur) tip 70, but theimplant design and subject's anatomy predicts this pathway will likelyresult in a valgus malreduction. Once an optimal entry point isquantified 76, it is displayed on all subsequent images acquired. Theentry point is dynamically tracked image to image by the anatomical andimplant segmentation tracking module. The updated entry point iscalculated relative to the known and selected anatomical featuretracking points.

Now referring to FIG. 18D, a new image is acquired and the guidewire 77or starter reamer is recognized on the image. An ideal system predictedentry point is recommended and displayed 76, and if the user acceptsthese suggestions, the user then places and orientates the guidewire orreamer in the positions guided by the accepted and displayed virtual oraugmented grid or avatar 78. The user completes this step of theprocedure with imaging or CAS (traditional Computer Assisted Surgerysystem) tracking and intelligence guidance from the systems' algorithmicmodules.

Now referring to FIG. 19A, this step is defined by the lag-screwplacement. The lag-screw module will calculate and provide guidance onthe depth, and rotation, of nail placement 80. Once the nail is begun tobe inserted, the lag-screw placement 81 will be used as the determinantof the depth the nail needs to be seated in the bone. When an image isacquired, the artificial intelligence intra-operative surgical guidancesystem 1 uses the hip application segmentation machine learning moduleto identify the relevant anatomical, instrument, and implant featuressuch as the nail alignment jig, and nail and lag-screw.

In addition, the artificial intelligence intra-operative surgicalguidance system 1 provides an automatic determination of screwtrajectories and more generally in situations of instrumentationtrajectories. For example, to determine if the instrumentation is withinthe right plane while simultaneously tracking anatomical, implant andinstrument considerations in different views. This is achieved usingdeep learning techniques, more specifically, a Reinforcement Learning(RL) technique. RL strategies are used to train an artificial RL agentto precisely and robustly identify/localize the optimal trajectory/pathby navigating in an environment, in our case the acquired fluoroscopicimages. The agent makes decisions upon which direction it has to proceedtowards an optimal path. By using such a decision-making process, the RLagent learns how to reach the final optimal trajectory. An RL agentlearns by interacting with an environment E. At every state (S), theregion of interest in this situation, a single decision is made tochoose an action (A), which consists of modifying the coordinates of thetrajectory, from a set of multiple discrete actions (A). Each validaction choice results in an associated scalar reward, defining thereward signal (R). The agent attempts to learn a policy to maximize bothimmediate and subsequent future rewards. The reward encourages the agentto move towards the best trajectory while still being learnable. Withthese considerations, we define the reward R=sgn(ED(Pi−1, Pt)−D(Pi,Pt)), where D is a function taking the Euclidean distance between planeparameters. Pi (Px, Py, Pz) is the current predicted trajectorycoordinate at step 1 and Pt is the target ground truth coordinates. Thedifference of the parameter distances, between the previous and currentsteps, signifies whether the agent is moving closer to or further awayfrom the desired plane parameters. Finally, the terminate state isreached once the RL agent reaches the target plane coordinates. The taskis to learn an optimal policy that maximizes the intermediate rewardsbut also to subsequent future rewards.

Now referring to FIG. 19B, the nail is tracked in real-time usingsensors or images and is guided to the correct depth and the lag-screwconfiguration and placement information is provided as an input forinterpretation. The location of the lag-screw in the specific anatomy 82is analyzed to statistically determine its probability of cutting out ofthe femoral head 83.

Now referring to FIG. 20A, the failure mode and a risk score iscalculated and can be displayed to a user. Based upon the calculations,the artificial intelligence intra-operative surgical guidance system 1predicts an optimal position of the implant and provide guidance forplacement 85. In the event of a malrotation situation in a nailingprocedure, predictive support and guidance can be quantified for optimalrotational correction and implant placement 86.

Now referring to FIG. 20B, in the event of a fracture or deformitycorrection situation that requires plating and screw fixation, the useof multivariate relationship datasets can provide the user with impairedhealing predictions 90 such as in the case of which plate to use. Oralternatively a screw combination calculation can provide the user witha prediction 91 of a consistent result if a specific guidance isfollowed. This can provide a predictive output of a normal healing orabnormal healing expectation 92.

Now referring to FIG. 21 , the real-time situational guidance workflowof a plate and screw application is depicted displaying the inputs,tasks and actions performed, and the outputs with a graphical userinterface display. As the user/subject navigates through the procedure,the artificial intelligence intra-operative surgical guidance system 1provides awareness support to situations encountered and providesperformance and complication avoidance recommendations at criticalpoints in the workflow 500. Specific decision points in the workflow canbe, for example, the screw order and placement position in the plateimplant 501. This can provide surgical guidance to the user/subject, forexample, for tracking to a drill guide 502.

Example—ankle injury. Now referring to FIG. 22 , a problem predictionand error prevention workflow is depicted for an ankle procedure. Nowreferring to FIG. 3 , the information from independent datasets can beaccessed at any given time, or alternatively a situation during theevent can require the input from various datasets simultaneously. Inthis situation information from the relevant datasets are selected forinclusion in the weighted multivariate relationship data model. Thismodel utilizes information from datasets that have a relationship fromthe perspective of sharing predictors for example of a specific outcome.The model further weights the data based upon the level of criticalityregarding performance or failure for example. The model outputs decisionsupport and outcome predictions for example the probability of asuccessful and optimal long-term outcome. In the event of a fracture orsyndesmosis or fibula minus situation 600 that requires fixation 601,the use of multivariate relationship datasets and outcomes predictionmodules can provide the user with impaired healing predictions 602 suchas in the case of which implant to use. Or alternatively an implantcombination calculation can provide the user with a prediction of aconsistent result if a specific guidance is followed. This can provide apredictive output of an optimal implant position or normalhealing/abnormal healing expectation and probability of success 603.

Example—Hip Arthroplasty. Now referring to FIG. 23 , in the event of ahip replacement situation 610 that requires implant fixation 611, theuse of multivariate relationship datasets and outcomes predictionmodules can provide the user with impaired healing predictions 612 suchas in the case of which implant to use. Or alternatively an implantorientation calculation can provide the user with a prediction of aconsistent result if a specific guidance is followed. This can provide apredictive output of an optimal implant position or normalhealing/abnormal healing expectation and probability of success 613.

Example—Knee Arthroplasty. Now referring to FIG. 24 , in the event of aknee replacement situation 620 that requires implant fixation 621, theuse of multivariate relationship datasets, sensor information,multi-modality medical images, and outcomes prediction modules canprovide the user with hazard and failure predictions 622 such as in thecase of what resection or balancing needs to be performed. Oralternatively an implant alignment calculation can provide the user witha prediction of a consistent result if a specific guidance is followed.This can provide a predictive output of an optimal implant position ornormal healing/abnormal healing expectation and probability of success623.

Example—Spine. Now referring to FIG. 25 in the event of a spinesituation 630 that requires fixation 631, the use of multivariaterelationship datasets and outcomes prediction modules can provide theuser with impaired healing predictions 632 such as in the case of whichimplant to use. Or alternatively an implant placement calculation canprovide the user with a prediction of a consistent result if a specificguidance is followed. This can provide a predictive output of an optimalimplant position or normal healing/abnormal healing expectation andprobability of success 633.

Example—Sports Medicine. Now referring to FIG. 26A in the event of asports medicine situation 640 that requires alignment or fixationguidance 641, the use of multivariate relationship datasets and outcomesprediction modules can provide the user with impaired healingpredictions 642 such as in the case of which implant to use. Oralternatively an implant placement calculation or soft tissue managementcan provide the user with a prediction of a consistent result if aspecific guidance is followed. This can provide a predictive output ofan optimal implant position or normal healing/abnormal healingexpectation and probability of success 643.

Now referring to FIG. 26B in the event of a PAO/FAI situation 655 thatrequires alignment and fixation 656, the use of multivariaterelationship datasets and outcomes prediction modules can provide theuser with impaired healing predictions 657 such as in the case of whichimplant to use. Multivariate relationship datasets is defined asanalysis performed using interaction between different fields anddifferent datasets. Or alternatively an implant placement calculationcan provide the user with a prediction of a consistent result if aspecific guidance is followed. This can provide guidance for a robot ora predictive output of an optimal implant position or normalhealing/abnormal healing expectation and probability of success 658.

Now referring to FIG. 27 , the generic workflow of an outcome predictiondemonstrates the predictive probability method in calculating thelikelihood of a successful long-term outcome. Datasets configured toinclude information that will potentially have an impact on the outcomeof the procedure are accessed. The datasets are used to interpretcritical failure mode factors of an implant or anatomical alignment andwhen used to train an outcomes classifier for an Artificial IntelligenceEngine will provide the user with a prediction of optimal or suboptimaloutcome and an associated Failure Risk Score. Multiple classifiers canbe constructed from multiple datasets and used in a single AI Engine.

Information from the relevant datasets are selected for inclusion in theArtificial Intelligence (AI) Engine in the form of multiple trainedclassifiers, each with a weighted contribution to the final surgicaloutcome prediction. This multiple prediction model uses information fromdatasets that have a relationship from the perspective of sharinguncorrelated or partially correlated predictors of a specific outcome.The AI Engine can further weight the outcome prediction data based uponthe relative level of criticality regarding performance or failure. Themodel outputs decision support and outcome predictions for example theprobability of a successful and optimal long-term outcome 665.

Now referring to FIG. 28A, the factors affecting the output of a nailingapplication demonstrates the calculation of the likelihood of anon-union of the bone post procedure. Now referring to FIG. 28B, theweighted model provides an at least one graphical surgical guidancethrough a graphical user interface output demonstrating the predictionof an optimal versus sub-optimal outcome. The weighting factors 96 canbe demonstrated to the user in addition to an implant and outcomeperformance percentage metric 97 predicting ultimately the probabilityof a good outcome. Now referring to FIG. 28C, the workflow demonstratingthe iterative decision making and support process by the artificialintelligence intra-operative surgical guidance system 1 and AI modelinterfacing with the user. The images or information can be compiled andsaved to a user system or device of choice, such as a PAC's system,Cloud or RAM.

Now referring to FIG. 29 , the information or images can be saved forsubject demonstration post-procedure as a graphical user interface. Theartificial intelligence intra-operative surgical guidance system 1 isconfigured to support a tool that will utilize AI to automate thedetection, tracking, monitoring and performance/complication predictionfor user's post-procedure.

Now referring to FIG. 30 , the data from the procedure can be used forimplant performance, tracking, outcome knowledge, outcome knowledge,inventory optimization, Quality Assurance, and design optimization. thedata from the procedure can be saved for outcomes analysis and scoringafter an event such as surgery for the following: 1) user performanceand usage information, 2) tracking and monitoring performance andoutcome metrics—for example IoT monitoring of alignment and reduction,and implant fixation and the prediction of a roadmap to a successfuloutcome, and 3) subject, event or situation predictors, indicators,factors, and variables. Surgical guidance such as measurements and datacan also be sent to: a surgical facilitator 160 such as a feedbackdevice, a robot, a tracked Implant or object, a cutting block, a CAS anda IoT device

Measurements and data can also be sent to a touch/force sensor to amixed/augmented/holographic reality device 167 showing visualization,alignment, and placement of instruments, bones or implants in 2D/3D.mixed/augmented/holographic reality image/shape model with thedimensioned grid in a surgical environment in real-time intraoperativelyby projecting mixed/augmented reality grid data and image measurementsfor live dynamic mixed/augmented reality tracking of the dimensionedgrid, surgical instruments and implant.

Now referring to FIG. 31 a grid data predictive map 301 is shown. Thecomputing platform 100 identifies a best-matching nonoperative sideimage as compared to a current operative-side image using an imagesimilarity metric; registering the best-matching non-operative sideimage to the current operative side image; and aligning thenon-operative-side image with the current operative side image toprovide a guidance pose-guide image, wherein the guidance pose-guideimage graphically illustrates the difference in the anatomicalpositioning of the non-operative and operative-side images as shown. Agrid data predictive map 301 is a grid of points of interest in theimage i.e., the coordinates position of landmarks.

The grid data predictive map 301 is an overlay image wherein thered-green-blue pixel values of the overlay are computed using a colormap that maps surgical outcome classification values to color hues. Afirst color for sub-optimal positioning and second color for optimalpositioning is provided. The classifications, in this case, are inreference to locations in the grid data predictive map 301 that areassociated with optimal or suboptimal positioning of implants 302,instrumentation, or bone positioning in fracture reduction. In such anoverlay, for example, the color mapping may be a “heat map” wheresuboptimal positioning regions on the grid map are indicated in red andoptimal positioning regions indicated with green. Such a grid datapredictive map 301 can be used for example, to guide optimal position ofa nail entry point. Other examples may include screw trajectories 303and implant positioning.

In practice a grid map of pixels/voxels contributes to predictive classby providing real-time situation awareness/decision support andgenerating a Risk Factor Score and predicts outcomes. A method forproviding surgical guidance to a user is provided including the stepsof: receiving an intra-operative image of a subject; generating a griddata predictive map; wherein the grid data predictive map is generatedby an artificial intelligence engine made of: computer algorithms anddata structures for storage and retrieval of post-operative medicalimages and associated metadata; aligning the intra-operative image withthe grid data predictive map to generate a graphical surgical guidanceindicator. The graphical surgical guidance indicator is dynamic in thatas the intraoperative images change to reflect changes in positioningthe color of the guidance indicator changes. In one exemplaryembodiment, grid data predictive map is made of a first color forsub-optimal positioning and second color for optimal positioning.

The foregoing detailed description has been presented for purposes ofillustration and description. It is not intended to be exhaustive or tolimit the invention to the precise form disclosed. Many modificationsand variations are possible considering the above teachings. Thedescribed embodiments were chosen to best explain the principlesinvolved and their practical applications, to thereby enable othersskilled in the art to best utilize the various embodiments and withvarious modifications as are suited to the use contemplated. It isintended that the scope of the invention be defined by the claimsappended hereto.

We claim: 1.-35. (canceled)
 36. An artificial intelligence basedintra-operative surgical guidance system comprising: a computingplatform comprised of an at least one image processing algorithm for theclassification of a plurality of intra-operative fluoroscopic medicalimages, said computing platform configured to execute one or moreautomated artificial intelligence models wherein the one or moreautomated artificial intelligence models comprises a neural networkmodel providing a score for the classification for surgical outcomes,wherein the one or more automated artificial intelligence models aretrained on data from a data layer, wherein the data layer includes atleast a plurality of fluoroscopic surgical images, wherein the automatedartificial intelligence models are trained to calculate intra-operativesurgical decision risks and a visual display configured to show asurgical outcome prediction based on calculated intra-operative surgicaldecision risks to a user.
 37. The artificial intelligence basedintra-operative surgical guidance system of claim 36, wherein thecomputing platform is comprised of an at least one of: an at least oneimage processing algorithm for the classification of a plurality ofintra-operative medical fluoroscopic images of a reduction or analignment procedure into at least one discrete category, wherein the atleast one discrete category is predictive of the surgical outcomeprediction; an at least one image processing algorithm for theclassification of a plurality of an intra-operative medical fluoroscopicimages of an implant fixation procedure into an at least one discretecategory, wherein the at least one discrete category is predictive ofthe surgical outcome prediction and an at least one image processingalgorithm for the classification of a time series of postoperativemedical fluoroscopic images into an at least one discrete category,wherein the at least one discrete category is predictive of the surgicaloutcome.
 38. An artificial intelligence based intra-operative surgicalguidance system configured to provide intra-operative surgical decisionrisks comprising: a non-transitory computer-readable storage mediumencoded with computer-readable instructions which form a software moduleand a processor to process the instructions, wherein the software moduleis comprised of a data layer, an algorithm layer and an applicationlayer, wherein the artificial intelligence based intra-operativesurgical guidance system is trained to calculate intra-operativesurgical decision risks by applying an at least one classifieralgorithm, wherein the algorithm layer is comprised of an at least oneimage processing algorithm for the classification of a plurality ofintra-operative fluoroscopic medical images, wherein the computingplatform is comprised-of an at least one of: an at least one imageprocessing algorithm for the classification of a plurality ofintra-operative medical fluoroscopic images of a reduction or analignment procedure into at least one discrete category, and an at leastone image processing algorithm for the classification of a plurality ofan intra-operative medical fluoroscopic images of an implant fixationprocedure into an at least one discrete category; and a visual displayconfigured to show a surgical outcome prediction based on calculatedintra-operative surgical decision risks to a user.
 39. The artificialintelligence based intra-operative surgical guidance system of claim 38wherein the algorithm layer comprises: an Image Quality Scoring Moduleconfigured to compute an image quality score for a plurality of acquiredfluoroscopic medical images; a Distortion Correction Module configuredto correct distortion in the acquired fluoroscopic medical image; anImage Annotation Module configured to annotate an at least oneanatomical landmark in a pre-operative fluoroscopic image to provide anat least one annotated pre-operative fluoroscopic image; a preoperativeimage database configured to store the at least one annotatedpre-operative fluoroscopic image; a 3D Shape Modeling Module configuredto estimate a three-dimensional shape of an implant or an anatomy; anArtificial Intelligence Engine comprised of an image processingalgorithm for the classification of an intra-operative fluoroscopicmedical image; an Image Registration Module configured to map analignment grid to the annotated image features to form a compositefluoroscopic image and an outcome module configured to intra-operativelyprovide a surgical outcome prediction to a user.
 40. The artificialintelligence based intra-operative surgical guidance system of claim 39wherein the image quality score is computed based on one or moreautomated artificial intelligence models quantifying a level of accuracyof an acquired fluoroscopic image.
 41. The artificial intelligence basedintra-operative surgical guidance system of claim 40 wherein thealgorithm layer is comprised of an at least one algorithm forregistering fluoroscopic images of a preoperative nonoperative side andfluoroscopic images of an intra-operative operative side to a commoncoordinate system.
 42. The artificial intelligence based intra-operativesurgical guidance system of claim 38, further comprising an outcomeclassifier for an artificial intelligence engine trained by a deeplearning model on at least one dataset used to interpret a plurality ofcritical failure mode factors of an implant or an anatomical alignment.43. The artificial intelligence based intra-operative surgical guidancesystem of claim 38 further comprising an outcome classifier for anartificial intelligence engine trained by a reinforcement learning modelon datasets to interpret critical failure mode factors of an implant.44. The artificial intelligence based intra-operative surgical guidancesystem of claim 38 further comprising an outcome classifier for anartificial intelligence engine trained by a reinforcement learning modelon datasets to determine screw trajectories.
 45. The artificialintelligence based intra-operative surgical guidance system of claim 38further comprising an outcome classifier for an artificial intelligenceengine trained by a reinforcement learning model on datasets tointerpret critical failure mode factors of screw placement.
 46. Theartificial intelligence based intra-operative surgical guidance systemof claim 36, further comprising a surgical facilitator synchronized withsaid computing platform.
 47. The artificial intelligence basedintra-operative surgical guidance system of claim 36 wherein thecomputing platform is configured to provide resection guidance based onhazard and failure predictions, wherein the hazard and failurepredictions are calculated from data on the data layer.
 48. Theartificial intelligence based intra-operative surgical guidance systemof claim 36 wherein an application within the computing platformregisters a grid template from an anatomical landmark from an opposingimage, wherein the grid template is a representation of the anatomicallandmark from the opposing image, wherein the visual display isconfigured to show the grid template superimposed over anintra-operative fluoroscopic image.
 49. The artificial intelligencebased intra-operative surgical guidance system of claim 36 wherein thecomputing software is configured to utilize an auto-segmentationalgorithm on the algorithm layer, whereby the auto-segmentationalgorithm selects the anatomical landmark from the opposing image toregister the grid template.
 50. The artificial intelligence basedintra-operative surgical guidance system of claim 36 wherein thecomputing platform is configured to derive the surgical outcomeprediction from a prediction information dataset and a prediction ofknown complications dataset on the data layer; wherein the algorithmlayer includes a segmentation machine learning algorithm that identifiesanatomical landmarks on the intra-operative fluoroscopic image; whereinthe segmentation machine algorithm utilizes information from an implantdatabase; wherein an algorithm on the algorithm layer registers asubject best entry-point grid template on the intra-operativefluoroscopic image; and wherein the computing platform is configuredprovide on the visual display the subject best entry-point grid templatefor a nail-entry site and further includes an at least one performanceprediction of nail-entry site.
 51. The artificial intelligence basedintra-operative surgical guidance system of claim 50 wherein a deeplearning algorithm on the algorithm layer registers the subject bestentry-point grid template on the intra-operative fluoroscopic image. 52.The artificial intelligence based intra-operative surgical guidancesystem of claim 36 the computing platform is configured to synchronizewith a surgical facilitator.
 53. The artificial intelligence basedintra-operative surgical guidance system of claim 52 wherein thesurgical facilitator is a sensor for providing input data to the datalayer of the computing platform.
 54. The artificial intelligence basedintra-operative surgical guidance system of claim 52 wherein thecomputing platform configured to synchronize with a surgical facilitatoris configured to communicate at least one of a surgical guidancedirective to the surgical facilitator.
 55. The artificial intelligencebased intra-operative surgical guidance system of claim 43 furthercomprising a preoperative image database. 56 (new) The artificialintelligence based intra-operative surgical guidance system of claim 36further comprising a module for generating a three-dimensionalstatistical model from a two-dimensional image of a subject's anatomy.57. The artificial intelligence based intra-operative surgical guidancesystem of claim 36 further comprising an automated image segmentationalgorithm to identify an anatomical structure in said plurality ofintra-operative fluoroscopic surgical images.